This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Sayeed, M. Articles by Karonis, N. T. Search for Related Content Social Bookmarking                   What's this?

GRID-enabled Solution of Groundwater Inverse Problems on the TeraGrid Network

Purdue University msayeed{at}purdue.edu

Kumar Mahinthakumar

North Carolina State University gmkumar{at}ncsu.edu

Nicholas T. Karonis

Northern Illinois University/Argonne National Laboratory karonis{at}niu.edu

Grid-computing environments are becoming increasingly popular for scientific computing due to the significant increase in capacity that they represent when compared to a single computational resource (e.g., a single cluster), their ubiquitous availability and advances in grid middleware components. Several such specialized grid environments are now available for users in the commercial and research sectors. One such effort for research is the TeraGrid, consisting of a collection of geographically distributed heterogeneous supercomputer resources including data storage resources. Parallel implementations for these environments are inherently multilevel and obtaining efficient mapping of work to processors can be eXtremely challenging. This paper eXtends an eXisting MPI application to the grid via the use of grid-enabled MPI libraries. The application uses a simulation-optimization framework involving coarse-grained parallelism in the optimizer and fine-grained parallelism in the finite-element-based simulator. Using parallelism at both these levels is essential for problems involving computationally intensive simulation steps. A hierarchical grid architecture consisting of a collection of supercomputers is ideally suited for these types of problems as a good application-to-architecture mapping can be obtained with a proper implementation. This paper presents the performance results of our implementation on the TeraGrid network consisting of three geographically distributed supercomputers.

Key Words: High performance computing • grid computing • inverse problems • optimization • groundwater • genetic algorithms • finite element methods • TeraGrid

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?